An important problem in forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application-specific, labor-intensive, and subjective. Purely data-driven methods have yet to be fully realized due in part to the lack of a sufficiently rich data source. However, high-throughput sequencing technologies are able to identify tens of thousands of microbial taxa using DNA recovered from a single swab collected from nearly any object or surface. We present a new algorithm for geolocation that aggregates over an ensemble of deep neural network classifiers trained on randomly-generated Voronoi partitions of a spatial domain. We apply the algorithm to fungi present in each of 1300 dust samples collected across the continental United States and then to a global dataset of dust samples from 28 countries. Our algorithm makes remarkably good point predictions with more than half of the geolocation errors under 100 kilometers for the continental analysis and nearly 90% classification accuracy of a sample's country of origin for the global analysis. We suggest that the effectiveness of this model sets the stage for a new, quantitative approach to forensic geolocation.

Vikram Mahidhar reminds us all that AI is only as good as the humans supervising it and programming it. The biases and artefacts that come out of the processing are reflective of the biases programmed in at the beginning. A program trained to recognise totalled car bodies for insurance purposes, for example, will need close supervision of its decision-making outputs, for regulatory and consumer confidence and acceptance of the decision. There is a call and a growth in a new class of AI--one that is explainable, and that builds trust by providing evidence. Vikram also reminds us that a governance strategy is key to engendering trust in our organisation, processes and people.

Data scientists are one of the most hirable specialists today, but it's not so easy to enter this profession without a "Projects" field in your resume. Furthermore, you need the experience to get the job, and you need the job to get the experience. Seems like a vicious circle, right? Also, the great advantage of data science projects is that each of them is a full-stack data science problem. Additionally, this means that you need to formulate the problem, design the solution, find the data, master the technology, build a machine learning model, evaluate the quality, and maybe wrap it into a simple UI.

Boaty McBoatface may be better known for its name than for its oceangoing prowess. But the autonomous underwater vehicle and darling of the internet is headed to greater things: embarking on the longest journey of an AUV by far, with an uninterrupted, roughly 2,000-mile crossing of the Arctic Ocean. The submersible robot got its moniker when it became the consolation prize in a 2016 publicity stunt. The United Kingdom's Natural Environmental Research Council had created an online poll to name the country's new polar research ship. The public picked "Boaty McBoatface" (suggested by a BBC radio announcer), but the British government nixed the idea and named the ship after naturalist David Attenborough.

When Detroit-based luxury goods brand Shinola began working on its new Vinton watch, the team designed with a woman in mind, but testing the product through analytics platform MakerSights, which correlates consumer feedback with historical sales data, revealed the style appealed to all genders. As a result, the brand deepened its buy-in on those by about 70 per cent. "You never design by data," says Shinola CEO Tom Lewand, "but the data provides a compass as you're navigating a hunch." In other words, Shinola already had a great vision – and the data enhanced it. MakerSights is among a new class of data-driven analytics platforms that combine factors such as search queries, social media activity, e-commerce sell-throughs and consumer feedback to provide clues into what is most likely to become a trend.

On Wednesday, a polar vortex brought bitter cold to the Midwest. Overnight, Chicago reached a low of 21 degrees Fahrenheit below zero, making it slightly colder than Antarctica, Alaska, and the North Pole. Wind chills were 64 degrees below zero in Park Rapids, Minnesota, and 45 degrees below zero in Buffalo, North Dakota, according to the National Weather Service. Schools, restaurants, and businesses closed, and more than 1,000 flights have been canceled. Even the United States Postal Service stalled mail delivery, temporarily.

A gigantic cavity two-thirds the area of Manhattan and almost 1,000 feet (300 meters) tall has been found growing at the bottom of Thwaites Glacier in West Antarctica. About the size of Florida, Thwaites Glacier is currently responsible for approximately 4 percent of global sea level rise. It holds enough ice to raise the world ocean a little over 2 feet (65 centimeters) and backstops neighboring glaciers that would raise sea levels an additional 8 feet (2.4 meters) if all the ice were lost. About the size of Florida, Thwaites Glacier is currently responsible for approximately 4 percent of global sea level rise. A gigantic cavity two-thirds the area of Manhattan and almost 1,000 feet (300 meters) tall has been found growing at the bottom of it.

NASA has spotted a second perfectly rectangular iceberg in the Antarctic. The second rectangular berg, known as a'tabular' iceberg, was spotted off the east coast of the Antarctic Peninsula, near the Larsen C ice shelf and close to the first one. It is part of a large'field of bergs NASA experts may have recently broken off the shelf, and say the sharp angles and flat surfaces are evidence the break occurred very recently. Just past the original rectangular iceberg, which is visible from behind the outboard engine, IceBridge saw another relatively rectangular berg and the A68 iceberg in the distance. Tabular icebergs split off the edges of ice shelves in the same way a fingernail that grows too long ends up cracking off.

Climate change has a direct impact on the agrarian societies, the frequency of natural disasters and the overall ecology of the planet. Using artificial intelligence, countries like Norway and India have made significant development in increasing their crop yields (30% increase in groundnut yields) and production of flexible and autonomous renewable energy grids and circuits. AI has also helped scientists map cyclones, atmospheric rivers, and weather fronts with 89 to 99 percent accuracy. These things were often hard to identify and predict beforehand, until now. Today, issues like water conservation, agriculture, biodiversity, and climate change are increasingly getting addressed by AI-powered terrestrial machines and geospatial satellites.

The ability to conduct logical reasoning is a fundamental aspect of intelligent behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities. More recently, however, there has been an increasing interest in using machine learning rather than logic-based formalisms to tackle these tasks. In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform basic ontology reasoning. This is an important and at the same time very natural reasoning problem, which is why the presented approach is applicable to a plethora of important real-world problems. We present the outcomes of several experiments, which show that our model learned to perform precise reasoning on diverse and challenging tasks. Furthermore, it turned out that the suggested approach suffers much less from different obstacles that prohibit symbolic reasoning, and, at the same time, is surprisingly plausible from a biological point of view.